Austin Peck

The antifungal agent benomyl [methyl-1-(butylcarbamoyl)-2-benzimidazolecarbamate] is used throughout the world against a wide range of agricultural fungal diseases. In this paper, we investigated the interaction of benomyl with mammalian brain tubulin and microtubules. Using the hydrophobic fluorescent probe 1-anilinonaphthalene-8-sulfonic acid, benomyl was found to bind to brain tubulin with a dissociation constant of 11.9 +/- 1.2 microM. Further, benomyl bound to at a novel site, distinct from the well-characterized colchicine and vinblastine binding sites. Benomyl altered the far-UV circular dichroism spectrum of tubulin and reduced the accessibility of its cysteine residues to modification by 5,5'-dithiobis-2-nitrobenzoic acid, indicating that benomyl binding to tubulin induces a conformational change in the tubulin. Benomyl inhibited the polymerization of brain tubulin into microtubules, with 50% inhibition occurring at a concentration of 70-75 microM. Furthermore, it strongly suppressed the dynamic instability behavior of individual brain microtubules in vitro as determined by video microscopy. It reduced the growing and shortening rates of the microtubules but did not alter the catastrophe or rescue frequencies. The unexpected potency of benomyl against mammalian microtubule polymerization and dynamics prompted us to investigate the effects of benomyl on HeLa cell proliferation and mitosis. Benomyl inhibited proliferation of the cells with an IC(50) of 5 microM, and it blocked mitotic spindle function by perturbing microtubule and chromosome organization. The greater than expected actions of benomyl on mammalian microtubules and mitosis together with its relatively low toxicity suggest that it might be useful as an adjuvant in cancer chemotherapy.

The method of microtubule tracking and dynamics analysis, presented here, improves upon the current means of manual and automated quantification of microtubule behavior. Key contributions are increasing accuracy and data volume, eliminating user bias and providing advanced analysis tools for the discovery of temporal patterns in cellular processes. By tracking the entire length of each resolvable microtubule, as opposed to only the tip, it is possible to boost dynamics studies with positional information that is virtually impossible to collect manually. We demonstrate the method on the analysis of a microtubule dataset, which was manually tracked and analyzed in the study of betaIII-tubulin isoform. Our results show that automated recognition of temporal patterns in cellular processes offers a highly promising potential

We have utilized tau-assembled and tau-stabilized microtubules (MTs), in the absence of taxol, to investigate the effects of tau isoforms with three and four MT binding repeats upon kinesin-driven MT gliding. MTs were assembled in the presence of either 3-repeat tau (3R tau) or 4-repeat tau (4R tau) at tau:tubulin dimer molar ratios that approximate those found in neurons. MTs assembled with 3R tau glided at 31.1 μm/min versus 25.8 μm/min for 4R tau, a statistically significant 17% difference. Importantly, the gliding rates for either isoform did not change over a fourfold range of tau concentrations. Further, tau-assembled MTs underwent minimal dynamic instability behavior while gliding and moved with linear trajectories. In contrast, MTs assembled with taxol in the absence of tau displayed curved gliding trajectories. Interestingly, addition of 4R tau to taxol-stabilized MTs restored linear gliding, while addition of 3R tau did not. The data are consistent with the ideas that (i) 3R and 4R tau-assembled MTs possess at least some isoform-specific features that impact upon kinesin translocation, (ii) tau-assembled MTs possess different structural features than do taxol-assembled MTs, and (iii) some features of tau-assembled MTs can be masked by prior assembly by taxol. The differences in kinesin-driven gliding between 3R and 4R tau suggest important features of tau function related to the normal shift in tau isoform composition that occurs during neural development as well as in neurodegeneration caused by altered expression ratios of otherwise normal tau isoforms.

We present an automated method for the tracking and dynamics modeling of microtubules -a major component of the cytoskeleton- which provides researchers with a previously unattainable level of data analysis and quantification capabilities. The proposed method improves upon the manual tracking and analysis techniques by i) increasing accuracy and quantified sample size in data collection, ii) eliminating user bias and standardizing analysis, iii) making available new features that are impractical to capture manually, iv) enabling statistical extraction of dynamics patterns from cellular processes, and v) greatly reducing required time for entire studies. An automated procedure is proposed to track each resolvable microtubule, whose aggregate activity is then modeled by mixtures of Hidden Markov Models to uncover dynamics patterns of underlying cellular and experimental conditions. Our results support manually established findings on an actual microtubule dataset and illustrate how automated analysis of spatial and temporal patterns offers previously unattainable insights to cellular processes.

BACKGROUND: The dynamic growing and shortening behaviors of microtubules are central to the fundamental roles played by microtubules in essentially all eukaryotic cells. Traditionally, microtubule behavior is quantified by manually tracking individual microtubules in time-lapse images under various experimental conditions. Manual analysis is laborious, approximate, and often offers limited analytical capability in extracting potentially valuable information from the data. RESULTS: In this work, we present computer vision and machine-learning based methods for extracting novel dynamics information from time-lapse images. Using actual microtubule data, we estimate statistical models of microtubule behavior that are highly effective in identifying common and distinct characteristics of microtubule dynamic behavior. CONCLUSION: Computational methods provide powerful analytical capabilities in addition to traditional analysis methods for studying microtubule dynamic behavior. Novel capabilities, such as building and querying microtubule image databases, are introduced to quantify and analyze microtubule dynamic behavior.